class_docstr: Implements the log-odds-ratio with an uninformative dirichlet prior from
          Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008). Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372–403.
docstr: Parameters
  ----------
  priors : pd.Series
          term -> prior count
  
  sigma : np.float
          prior scale
  
  scale_type : str
          'none': Don't scale prior. Jurafsky approach.
          'class-size': Scale prior st the sum of the priors is the same as the word count
            in the document-class being scaled
          'corpus-size': Scale prior to the size of the corpus
          'word': Original formulation from MCQ. Sum of priors will be sigma.
          'background-corpus-size': Scale corpus size to multiple of background-corpus.
  
  prior_power : numeric
          Exponent to apply to prior
          > 1 will shrink frequent words
docstr_tok: ["Parameters","--","--","--","--","--","priors",":","pd.Series","term","-",">","prior","count","sigma",":","np.float","prior","scale","scale_type",":","str","'none","'",":","Do","n't","scale","prior",".","Jurafsky","approach",".","'class-size","'",":","Scale","prior","st","the","sum","of","the","priors","is","the","same","as","the","word","count","in","the","document-class","being","scaled","'corpus-size","'",":","Scale","prior","to","the","size","of","the","corpus","'word","'",":","Original","formulation","from","MCQ",".","Sum","of","priors","will","be","sigma",".","'background-corpus-size","'",":","Scale","corpus","size","to","multiple","of","background-corpus",".","prior_power",":","numeric","Exponent","to","apply","to","prior",">","1","will","shrink","frequent","words"]
code: def __init__(
      self, priors, sigma=10, scale_type="none", prior_power=1
  ):
      """
      Parameters
      ----------
      priors : pd.Series
              term -> prior count
  
      sigma : np.float
              prior scale
  
      scale_type : str
              'none': Don't scale prior. Jurafsky approach.
              'class-size': Scale prior st the sum of the priors is the same as the word count
                in the document-class being scaled
              'corpus-size': Scale prior to the size of the corpus
              'word': Original formulation from MCQ. Sum of priors will be sigma.
              'background-corpus-size': Scale corpus size to multiple of background-corpus.
  
      prior_power : numeric
              Exponent to apply to prior
              > 1 will shrink frequent words
  
      """
      assert scale_type in [
          "none",
          "class-size",
          "corpus-size",
          "background-corpus-size",
          "word",
      ]
      self._priors = priors
      self._scale_type = scale_type
      self._prior_power = prior_power
      self._scale = sigma
      LogOddsRatioUninformativeDirichletPrior.__init__(self, sigma)
  
file_path: scattertext/scattertext/termsignificance/LogOddsRatioInformativeDirichletPiror.py
imports: import numpy
  from scipy.special import ndtr
  from scattertext.termsignificance import LogOddsRatioUninformativeDirichletPrior
label_desc: Use image node_id 1 to create a new LogOddsRatioInformativeDirichletPrior object from inherited base classes: LogOddsRatioUninformativeDirichletPrior with example: obj = LogOddsRatioInformativeDirichletPrior(priors, sigma, scale_type, prior_power)